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Semi-automatic Feature-Adaptive Relevance Feedback (SA-FR-RF) for Content-Based Image Retrieval

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Visual Information and Information Systems (VISUAL 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3736))

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Abstract

The paper proposes an adaptive retrieval approach based on the concept of relevance-feedback, which establishes a link between high-level concepts and low-level features. The user’s feedback is used not only to assign proper weights to the features, but also to dynamically select them and to identify the set of relevant features according to a user query, maintaining at the same time a small sized feature vector to attain better matching and lower complexity. Results achieved on a large image dataset show that the proposed algorithm outperforms previously proposed methods. Further, it is experimentally demonstrated that it approaches the results obtained by optimum feature selection techniques having complete knowledge of the data set.

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Author information

Authors and Affiliations

  1. Dept. of Information and Communication Technology, University of Trento, Italy

    Anelia Grigorova & Francesco G. B. De Natale

Authors
  1. Anelia Grigorova
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  2. Francesco G. B. De Natale
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Editor information

Editors and Affiliations

  1. LIRIS, INSA-Lyon, F-69621, France

    Stéphane Bres

  2. LIRIS UMR 5205 CNRS, INSA-Lyon, F-69621, France

    Robert Laurini

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© 2006 Springer-Verlag Berlin Heidelberg

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Grigorova, A., De Natale, F.G.B. (2006). Semi-automatic Feature-Adaptive Relevance Feedback (SA-FR-RF) for Content-Based Image Retrieval. In: Bres, S., Laurini, R. (eds) Visual Information and Information Systems. VISUAL 2005. Lecture Notes in Computer Science, vol 3736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590064_14

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  • DOI: https://doi.org/10.1007/11590064_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30488-3

  • Online ISBN: 978-3-540-32339-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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